Learn how to leverage OpenSearch as a powerful vector database solution for AI applications in this 30-minute conference talk. Discover the evolution from traditional lexical search to vector-based similarity search and understand how OpenSearch combines both approaches in a comprehensive package. Explore fundamental concepts of vector databases, including how they store and process embedded meanings of text, images, and audio using k-nearest neighbors (k-NN) functionality. Examine practical applications such as visual search, semantic search, and recommendation engines through real-world use cases. Understand how OpenSearch can serve as a knowledge base for AI systems, particularly in retrieval augmented generation (RAG) applications with large language models.